Overview

Dataset statistics

Number of variables14
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical1

Alerts

dbscan_labels has constant value ""Constant
Alcohol is highly overall correlated with Color_Intensity and 1 other fieldsHigh correlation
Ash is highly overall correlated with Ash_AlcanityHigh correlation
Ash_Alcanity is highly overall correlated with AshHigh correlation
Color_Intensity is highly overall correlated with Alcohol and 3 other fieldsHigh correlation
Flavanoids is highly overall correlated with Color_Intensity and 4 other fieldsHigh correlation
Hue is highly overall correlated with Color_Intensity and 3 other fieldsHigh correlation
Malic_Acid is highly overall correlated with HueHigh correlation
OD280 is highly overall correlated with Alcohol and 5 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
Total_Phenols is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-11-28 13:20:08.665389
Analysis finished2023-11-28 13:20:39.276923
Duration30.61 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.591287
Minimum11.03
Maximum14.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:41.249004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.61
Q112.08
median12.51
Q313.03
95-th percentile13.84
Maximum14.34
Range3.31
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.70213484
Coefficient of variation (CV)0.055763547
Kurtosis-0.37573269
Mean12.591287
Median Absolute Deviation (MAD)0.43
Skewness0.37416643
Sum1271.72
Variance0.49299333
MonotonicityNot monotonic
2023-11-28T13:20:41.463427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.37 6
 
5.9%
12.08 5
 
5.0%
12.29 3
 
3.0%
12.42 3
 
3.0%
12 3
 
3.0%
12.7 2
 
2.0%
12.25 2
 
2.0%
12.51 2
 
2.0%
11.82 2
 
2.0%
12.6 2
 
2.0%
Other values (66) 71
70.3%
ValueCountFrequency (%)
11.03 1
1.0%
11.41 1
1.0%
11.45 1
1.0%
11.46 1
1.0%
11.56 1
1.0%
11.61 1
1.0%
11.62 1
1.0%
11.64 1
1.0%
11.65 1
1.0%
11.66 1
1.0%
ValueCountFrequency (%)
14.34 1
1.0%
14.16 1
1.0%
14.13 1
1.0%
13.88 1
1.0%
13.86 1
1.0%
13.84 1
1.0%
13.78 1
1.0%
13.73 1
1.0%
13.69 1
1.0%
13.67 1
1.0%

Malic_Acid
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4124752
Minimum0.74
Maximum5.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:41.690181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.98
Q11.47
median2.12
Q33.27
95-th percentile4.43
Maximum5.04
Range4.3
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1357169
Coefficient of variation (CV)0.4707683
Kurtosis-0.83103813
Mean2.4124752
Median Absolute Deviation (MAD)0.83
Skewness0.54290076
Sum243.66
Variance1.2898528
MonotonicityNot monotonic
2023-11-28T13:20:41.906512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.51 3
 
3.0%
1.53 2
 
2.0%
3.43 2
 
2.0%
1.13 2
 
2.0%
1.67 2
 
2.0%
1.61 2
 
2.0%
3.17 2
 
2.0%
1.35 2
 
2.0%
1.29 2
 
2.0%
1.73 2
 
2.0%
Other values (80) 80
79.2%
ValueCountFrequency (%)
0.74 1
1.0%
0.89 1
1.0%
0.9 1
1.0%
0.92 1
1.0%
0.94 1
1.0%
0.98 1
1.0%
1.01 1
1.0%
1.07 1
1.0%
1.1 1
1.0%
1.13 2
2.0%
ValueCountFrequency (%)
5.04 1
1.0%
4.95 1
1.0%
4.72 1
1.0%
4.61 1
1.0%
4.6 1
1.0%
4.43 1
1.0%
4.36 1
1.0%
4.31 1
1.0%
4.3 1
1.0%
4.12 1
1.0%

Ash
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3227723
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:42.113544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.88
Q12.19
median2.31
Q32.5
95-th percentile2.74
Maximum3.23
Range1.87
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.29308742
Coefficient of variation (CV)0.12618001
Kurtosis1.0031588
Mean2.3227723
Median Absolute Deviation (MAD)0.17
Skewness-0.18107572
Sum234.6
Variance0.085900238
MonotonicityNot monotonic
2023-11-28T13:20:42.316768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 5
 
5.0%
2.3 4
 
4.0%
2.32 4
 
4.0%
2.38 4
 
4.0%
2.4 3
 
3.0%
1.92 3
 
3.0%
2.7 3
 
3.0%
2.48 3
 
3.0%
2.2 3
 
3.0%
1.98 3
 
3.0%
Other values (48) 66
65.3%
ValueCountFrequency (%)
1.36 1
 
1.0%
1.7 2
2.0%
1.71 1
 
1.0%
1.82 1
 
1.0%
1.88 1
 
1.0%
1.9 1
 
1.0%
1.92 3
3.0%
1.94 1
 
1.0%
1.98 3
3.0%
1.99 1
 
1.0%
ValueCountFrequency (%)
3.23 1
 
1.0%
2.92 1
 
1.0%
2.86 1
 
1.0%
2.78 1
 
1.0%
2.75 1
 
1.0%
2.74 2
2.0%
2.73 1
 
1.0%
2.72 1
 
1.0%
2.7 3
3.0%
2.67 1
 
1.0%

Ash_Alcanity
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.59802
Minimum10.6
Maximum28.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:42.519748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile16
Q118.5
median20.5
Q322.5
95-th percentile25
Maximum28.5
Range17.9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8882168
Coefficient of variation (CV)0.14021818
Kurtosis1.0782538
Mean20.59802
Median Absolute Deviation (MAD)2
Skewness0.056058811
Sum2080.4
Variance8.341796
MonotonicityNot monotonic
2023-11-28T13:20:42.705922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 9
 
8.9%
21 8
 
7.9%
18 8
 
7.9%
21.5 7
 
6.9%
19 7
 
6.9%
22 6
 
5.9%
18.5 6
 
5.9%
19.5 6
 
5.9%
22.5 6
 
5.9%
24 5
 
5.0%
Other values (22) 33
32.7%
ValueCountFrequency (%)
10.6 1
 
1.0%
15 1
 
1.0%
16 5
5.0%
16.8 1
 
1.0%
17.5 3
 
3.0%
18 8
7.9%
18.1 1
 
1.0%
18.5 6
5.9%
18.8 1
 
1.0%
19 7
6.9%
ValueCountFrequency (%)
28.5 2
 
2.0%
26.5 1
 
1.0%
26 1
 
1.0%
25.5 1
 
1.0%
25 3
3.0%
24.5 2
 
2.0%
24 5
5.0%
23.6 1
 
1.0%
23.5 1
 
1.0%
23 2
 
2.0%

Magnesium
Real number (ℝ)

Distinct35
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.70297
Minimum70
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:42.906620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80
Q186
median90
Q3100
95-th percentile113
Maximum134
Range64
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.221893
Coefficient of variation (CV)0.11976027
Kurtosis1.1322141
Mean93.70297
Median Absolute Deviation (MAD)6
Skewness0.97661196
Sum9464
Variance125.93089
MonotonicityNot monotonic
2023-11-28T13:20:43.084615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
88 13
 
12.9%
86 10
 
9.9%
85 6
 
5.9%
98 6
 
5.9%
80 5
 
5.0%
94 4
 
4.0%
92 4
 
4.0%
96 4
 
4.0%
103 3
 
3.0%
89 3
 
3.0%
Other values (25) 43
42.6%
ValueCountFrequency (%)
70 1
 
1.0%
78 3
 
3.0%
80 5
 
5.0%
81 1
 
1.0%
82 1
 
1.0%
84 3
 
3.0%
85 6
5.9%
86 10
9.9%
87 3
 
3.0%
88 13
12.9%
ValueCountFrequency (%)
134 1
 
1.0%
123 1
 
1.0%
122 1
 
1.0%
119 1
 
1.0%
116 1
 
1.0%
113 1
 
1.0%
112 3
3.0%
111 1
 
1.0%
108 1
 
1.0%
107 2
2.0%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0439604
Minimum0.98
Maximum3.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:43.279465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.35
Q11.62
median2
Q32.42
95-th percentile2.98
Maximum3.52
Range2.54
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.54434195
Coefficient of variation (CV)0.26631727
Kurtosis-0.1988977
Mean2.0439604
Median Absolute Deviation (MAD)0.39
Skewness0.55002373
Sum206.44
Variance0.29630816
MonotonicityNot monotonic
2023-11-28T13:20:43.493510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 7
 
6.9%
2 5
 
5.0%
1.7 3
 
3.0%
1.38 3
 
3.0%
1.65 3
 
3.0%
1.98 3
 
3.0%
1.48 3
 
3.0%
2.6 2
 
2.0%
2.74 2
 
2.0%
2.56 2
 
2.0%
Other values (56) 68
67.3%
ValueCountFrequency (%)
0.98 1
 
1.0%
1.15 1
 
1.0%
1.25 1
 
1.0%
1.28 1
 
1.0%
1.3 1
 
1.0%
1.35 1
 
1.0%
1.38 3
3.0%
1.39 2
2.0%
1.4 1
 
1.0%
1.41 1
 
1.0%
ValueCountFrequency (%)
3.52 1
1.0%
3.5 1
1.0%
3.18 2
2.0%
3.02 1
1.0%
2.98 1
1.0%
2.95 1
1.0%
2.9 1
1.0%
2.86 1
1.0%
2.85 1
1.0%
2.83 1
1.0%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6363366
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:43.697616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5
Q10.92
median1.59
Q32.17
95-th percentile3.03
Maximum5.08
Range4.74
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.86252272
Coefficient of variation (CV)0.52710592
Kurtosis1.3011776
Mean1.6363366
Median Absolute Deviation (MAD)0.65
Skewness0.79720175
Sum165.27
Variance0.74394545
MonotonicityNot monotonic
2023-11-28T13:20:43.900502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 3
 
3.0%
2.03 3
 
3.0%
1.69 2
 
2.0%
1.84 2
 
2.0%
1.59 2
 
2.0%
1.64 2
 
2.0%
0.83 2
 
2.0%
2.26 2
 
2.0%
0.66 2
 
2.0%
0.6 2
 
2.0%
Other values (68) 79
78.2%
ValueCountFrequency (%)
0.34 1
1.0%
0.47 2
2.0%
0.48 1
1.0%
0.49 1
1.0%
0.5 1
1.0%
0.51 1
1.0%
0.52 1
1.0%
0.56 1
1.0%
0.57 1
1.0%
0.58 2
2.0%
ValueCountFrequency (%)
5.08 1
1.0%
3.75 1
1.0%
3.18 1
1.0%
3.15 1
1.0%
3.1 1
1.0%
3.03 1
1.0%
2.99 1
1.0%
2.92 1
1.0%
2.86 1
1.0%
2.79 1
1.0%

Nonflavanoid_Phenols
Real number (ℝ)

Distinct33
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39594059
Minimum0.14
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:44.093469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.21
Q10.29
median0.4
Q30.5
95-th percentile0.6
Maximum0.66
Range0.52
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.12612833
Coefficient of variation (CV)0.31855368
Kurtosis-0.90897777
Mean0.39594059
Median Absolute Deviation (MAD)0.1
Skewness0.059403729
Sum39.99
Variance0.015908356
MonotonicityNot monotonic
2023-11-28T13:20:44.275753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.4 7
 
6.9%
0.43 7
 
6.9%
0.53 6
 
5.9%
0.24 5
 
5.0%
0.37 5
 
5.0%
0.26 5
 
5.0%
0.34 4
 
4.0%
0.5 4
 
4.0%
0.52 4
 
4.0%
0.48 4
 
4.0%
Other values (23) 50
49.5%
ValueCountFrequency (%)
0.14 1
 
1.0%
0.17 2
 
2.0%
0.19 1
 
1.0%
0.21 3
3.0%
0.22 2
 
2.0%
0.24 5
5.0%
0.25 1
 
1.0%
0.26 5
5.0%
0.27 4
4.0%
0.28 1
 
1.0%
ValueCountFrequency (%)
0.66 1
 
1.0%
0.63 2
 
2.0%
0.61 2
 
2.0%
0.6 3
3.0%
0.58 3
3.0%
0.56 1
 
1.0%
0.53 6
5.9%
0.52 4
4.0%
0.5 4
4.0%
0.48 4
4.0%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4163366
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:44.463308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.68
Q11.03
median1.38
Q31.71
95-th percentile2.35
Maximum3.58
Range3.17
Interquartile range (IQR)0.68

Descriptive statistics

Standard deviation0.55379007
Coefficient of variation (CV)0.39100173
Kurtosis1.8302854
Mean1.4163366
Median Absolute Deviation (MAD)0.35
Skewness0.95585831
Sum143.05
Variance0.30668345
MonotonicityNot monotonic
2023-11-28T13:20:44.671057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 6
 
5.9%
1.87 5
 
5.0%
1.56 4
 
4.0%
1.77 3
 
3.0%
0.83 3
 
3.0%
1.63 3
 
3.0%
1.03 3
 
3.0%
1.04 3
 
3.0%
1.25 3
 
3.0%
1.46 3
 
3.0%
Other values (54) 65
64.4%
ValueCountFrequency (%)
0.41 1
1.0%
0.42 1
1.0%
0.55 1
1.0%
0.62 1
1.0%
0.64 1
1.0%
0.68 1
1.0%
0.73 2
2.0%
0.75 1
1.0%
0.8 2
2.0%
0.81 1
1.0%
ValueCountFrequency (%)
3.58 1
1.0%
2.91 1
1.0%
2.81 1
1.0%
2.7 1
1.0%
2.49 1
1.0%
2.35 1
1.0%
2.29 1
1.0%
2.28 1
1.0%
2.08 2
2.0%
2.01 1
1.0%

Color_Intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6824752
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:44.873583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2
Q12.7
median3.8
Q35.7
95-th percentile10.26
Maximum13
Range11.72
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6590443
Coefficient of variation (CV)0.56787152
Kurtosis0.61055546
Mean4.6824752
Median Absolute Deviation (MAD)1.23
Skewness1.2024377
Sum472.93
Variance7.0705167
MonotonicityNot monotonic
2023-11-28T13:20:45.090743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9 3
 
3.0%
3.8 3
 
3.0%
2.8 3
 
3.0%
4.6 3
 
3.0%
5.7 2
 
2.0%
3.3 2
 
2.0%
2.06 2
 
2.0%
2.6 2
 
2.0%
2.45 2
 
2.0%
3.05 2
 
2.0%
Other values (69) 77
76.2%
ValueCountFrequency (%)
1.28 1
1.0%
1.74 1
1.0%
1.9 1
1.0%
1.95 2
2.0%
2 1
1.0%
2.06 2
2.0%
2.08 1
1.0%
2.12 1
1.0%
2.15 1
1.0%
2.2 1
1.0%
ValueCountFrequency (%)
13 1
1.0%
11.75 1
1.0%
10.8 1
1.0%
10.68 1
1.0%
10.52 1
1.0%
10.26 1
1.0%
9.899999 1
1.0%
9.7 1
1.0%
9.58 1
1.0%
9.4 1
1.0%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9190099
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:45.291590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.74
median0.9
Q31.08
95-th percentile1.36
Maximum1.71
Range1.23
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.252002
Coefficient of variation (CV)0.27421033
Kurtosis-0.18604657
Mean0.9190099
Median Absolute Deviation (MAD)0.17
Skewness0.45646217
Sum92.82
Variance0.06350501
MonotonicityNot monotonic
2023-11-28T13:20:45.500704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57 5
 
5.0%
1.23 5
 
5.0%
0.96 4
 
4.0%
0.75 4
 
4.0%
0.93 3
 
3.0%
0.86 3
 
3.0%
1.04 3
 
3.0%
0.89 3
 
3.0%
1.19 3
 
3.0%
1.12 2
 
2.0%
Other values (52) 66
65.3%
ValueCountFrequency (%)
0.48 1
 
1.0%
0.54 1
 
1.0%
0.55 1
 
1.0%
0.56 2
 
2.0%
0.57 5
5.0%
0.58 2
 
2.0%
0.59 1
 
1.0%
0.61 2
 
2.0%
0.62 1
 
1.0%
0.66 2
 
2.0%
ValueCountFrequency (%)
1.71 1
 
1.0%
1.45 1
 
1.0%
1.42 1
 
1.0%
1.38 1
 
1.0%
1.36 2
 
2.0%
1.33 1
 
1.0%
1.31 1
 
1.0%
1.27 1
 
1.0%
1.25 1
 
1.0%
1.23 5
5.0%

OD280
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3836634
Minimum1.27
Maximum3.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:45.706523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.33
Q11.75
median2.42
Q33
95-th percentile3.38
Maximum3.69
Range2.42
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.69035023
Coefficient of variation (CV)0.28961734
Kurtosis-1.2866919
Mean2.3836634
Median Absolute Deviation (MAD)0.64
Skewness0.01006275
Sum240.75
Variance0.47658345
MonotonicityNot monotonic
2023-11-28T13:20:45.916477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.33 3
 
3.0%
2.96 3
 
3.0%
1.82 3
 
3.0%
1.75 3
 
3.0%
2.78 3
 
3.0%
3.21 2
 
2.0%
2.06 2
 
2.0%
2.26 2
 
2.0%
2.77 2
 
2.0%
1.58 2
 
2.0%
Other values (68) 76
75.2%
ValueCountFrequency (%)
1.27 1
 
1.0%
1.29 2
2.0%
1.3 1
 
1.0%
1.33 3
3.0%
1.36 1
 
1.0%
1.42 1
 
1.0%
1.47 1
 
1.0%
1.51 2
2.0%
1.55 1
 
1.0%
1.56 1
 
1.0%
ValueCountFrequency (%)
3.69 1
1.0%
3.64 1
1.0%
3.57 1
1.0%
3.48 1
1.0%
3.39 1
1.0%
3.38 1
1.0%
3.33 1
1.0%
3.3 2
2.0%
3.28 1
1.0%
3.26 1
1.0%

Proline
Real number (ℝ)

Distinct64
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.9901
Minimum278
Maximum720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-28T13:20:46.130408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile342
Q1434
median520
Q3625
95-th percentile685
Maximum720
Range442
Interquartile range (IQR)191

Descriptive statistics

Standard deviation115.79849
Coefficient of variation (CV)0.22057271
Kurtosis-0.98524932
Mean524.9901
Median Absolute Deviation (MAD)100
Skewness-0.16705961
Sum53024
Variance13409.29
MonotonicityNot monotonic
2023-11-28T13:20:46.324596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 5
 
5.0%
520 5
 
5.0%
630 4
 
4.0%
625 4
 
4.0%
510 3
 
3.0%
660 3
 
3.0%
480 3
 
3.0%
495 3
 
3.0%
562 3
 
3.0%
450 3
 
3.0%
Other values (54) 65
64.4%
ValueCountFrequency (%)
278 1
1.0%
290 1
1.0%
312 1
1.0%
315 1
1.0%
325 1
1.0%
342 1
1.0%
345 2
2.0%
352 1
1.0%
355 1
1.0%
365 1
1.0%
ValueCountFrequency (%)
720 1
 
1.0%
714 1
 
1.0%
710 1
 
1.0%
695 2
 
2.0%
685 1
 
1.0%
680 5
5.0%
678 1
 
1.0%
675 2
 
2.0%
672 1
 
1.0%
660 3
3.0%

dbscan_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
5
101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 101
100.0%

Length

2023-11-28T13:20:46.496537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T13:20:46.649994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5 101
100.0%

Most occurring characters

ValueCountFrequency (%)
5 101
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 101
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 101
100.0%

Interactions

2023-11-28T13:20:35.610700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:08.965205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:11.475233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.193780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.263611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.513701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.508627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.555664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:25.077691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.670055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.704788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.658130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.626577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.776259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.143292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:11.770168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.365709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.459147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.677248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.678957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.719175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:25.362834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.839570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.862449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.818378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.817076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.910219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.283392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:12.004271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.519125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.616696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.811671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.823018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.856254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:25.631004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.987921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.010292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.957059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.954245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.075212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.446743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:12.290197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.685563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.795277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.983344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.982708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.008063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:25.937876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.160814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.160420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.112467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.115761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.226228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.631896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:12.579033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.849190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.961317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.148006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.154321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.161668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:26.184997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.328778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.316123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.264478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.270278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.409046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.807886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:12.853872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.014460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:17.131882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.301694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.313981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.308196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:26.404915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.480653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.472367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.425712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.435497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.576581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:09.955102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.042852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.172625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:17.327524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.461824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.466256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.494891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:26.563185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.645682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.635325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.581522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.587286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.752884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:10.114837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.255599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.325136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:17.486182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.606768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.616412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.700785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:26.716772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.786702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.767366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.744324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.735794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:36.936876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:10.255115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.442536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.472429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:17.737695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.760315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.763923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:23.850312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:26.878160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:28.937218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:30.908342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:32.891842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:34.872127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:37.182885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:10.418205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.592889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.640296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:17.897167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:19.914844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:21.922397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:24.112977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.054879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.093276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.063430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.050254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.031242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:37.470757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:10.665855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.757642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.821434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.055963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.074027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.082282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:24.390768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.216038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.241788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.204093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.208829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.183595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:37.753434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:10.908711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:13.912350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:15.969265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.202175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.214715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.251762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:24.625604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.355192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.392155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.348584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.348224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.340788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:38.024318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:11.195185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:14.057902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:16.113774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:18.363502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:20.356442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:22.398547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:24.861532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:27.506218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:29.531445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:31.485694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:33.488368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:20:35.473350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T13:20:46.777999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.0000.1110.1370.663-0.482-0.4650.1580.3290.127-0.532-0.3070.227-0.338
Ash0.1111.0000.6410.283-0.176-0.2710.3960.3020.226-0.207-0.0260.203-0.068
Ash_Alcanity0.1370.6411.0000.088-0.073-0.2320.0890.2720.186-0.0240.066-0.061-0.051
Color_Intensity0.6630.2830.0881.000-0.510-0.6460.3110.3210.218-0.664-0.3100.404-0.332
Flavanoids-0.482-0.176-0.073-0.5101.0000.527-0.181-0.382-0.4570.7730.700-0.4000.749
Hue-0.465-0.271-0.232-0.6460.5271.000-0.220-0.551-0.1680.5710.230-0.2240.356
Magnesium0.1580.3960.0890.311-0.181-0.2201.0000.237-0.064-0.348-0.1720.214-0.111
Malic_Acid0.3290.3020.2720.321-0.382-0.5510.2371.0000.235-0.326-0.2320.154-0.296
Nonflavanoid_Phenols0.1270.2260.1860.218-0.457-0.168-0.0640.2351.000-0.295-0.2040.169-0.270
OD280-0.532-0.207-0.024-0.6640.7730.571-0.348-0.326-0.2951.0000.536-0.3440.626
Proanthocyanins-0.307-0.0260.066-0.3100.7000.230-0.172-0.232-0.2040.5361.000-0.2050.593
Proline0.2270.203-0.0610.404-0.400-0.2240.2140.1540.169-0.344-0.2051.000-0.226
Total_Phenols-0.338-0.068-0.051-0.3320.7490.356-0.111-0.296-0.2700.6260.593-0.2261.000

Missing values

2023-11-28T13:20:38.390535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T13:20:39.019421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinedbscan_labels
4313.243.982.2917.51032.642.630.321.664.360.823.006805
5912.370.941.3610.6881.980.570.280.421.951.051.825205
6012.331.102.2816.01012.051.090.630.413.271.251.676805
6112.641.362.0216.81002.021.410.530.625.750.981.594505
6213.671.251.9218.0942.101.790.320.733.801.232.466305
6312.371.132.1619.0873.503.100.191.874.451.222.874205
6412.171.452.5319.01041.891.750.451.032.951.452.233555
6512.371.212.5618.1982.422.650.372.084.601.192.306785
6613.111.011.7015.0782.983.180.262.285.301.123.185025
6712.371.171.9219.6782.112.000.271.044.681.123.485105
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinedbscan_labels
16312.963.452.3518.51061.390.700.400.945.2800000.681.756755
16413.782.762.3022.0901.350.680.411.039.5800000.701.686155
16513.734.362.2622.5881.280.470.521.156.6200000.781.755205
16613.453.702.6023.01111.700.920.431.4610.6800000.851.566955
16712.823.372.3019.5881.480.660.400.9710.2600000.721.756855
16913.404.602.8625.01121.980.960.271.118.5000000.671.926305
17012.203.032.3219.0961.250.490.400.735.5000000.661.835105
17112.772.392.2819.5861.390.510.480.649.8999990.571.634705
17214.162.512.4820.0911.680.700.441.249.7000000.621.716605
17714.134.102.7424.5962.050.760.561.359.2000000.611.605605